Machine Learning and Cyber Systems for Diabetes

*Type 2 Diabetes - From Pathophysiology to Cyber Systems*

[52] Barnabé, Aline, Aléssio, Ana

Cláudia Morandi, Bittar, Luis Fernando, de Moraes Mazetto, Bruna, Bicudo, Angélica M, de Paula, Erich V, Höehr, Nelci Fenalti, & Annichino-Bizzacchi, Joyce M. (2015). Folate, Vitamin B12 and Homocysteine Status in the Post-Folic Acid Fortification Era in Different Subgroups of the Brazilian Population Attended to at a Public Health Care Center. *Nutrition journal, 14*(1), 1.

[53] Zheng, Miaoyan, Zhang, Meilin, Yang, Juhong, Zhao, Shijing, Qin, Shanchun, Chen, Hui, Gao, Yuxia, & Huang, Guowei. (2014). Relationship between Blood Levels of Methyl Donor and Folate and Mild Cognitive Impairment in Chinese Patients with Type 2 Diabetes: A Case-Control Study. *Journal of clinical biochemistry and* 

*nutrition, 54*(2), 122-128.

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**Chapter 17**

Approach

*Shula Shazman*

prediabetes patient**?**

**1. Introduction**

18.5–25 kg/m<sup>2</sup>

precision medicine, intermittent fasting

**Abstract**

Selecting Intermittent Fasting

Diabetes: A Machine Learning

Type to Improve Health in Type 2

Intermittent fasting (IF) is the cycling between periods of eating and fasting. The two most popular forms of IER are: the 5: 2 diet characterized by two consecutive or non-consecutive "fast" days and the alternate-day energy restriction, commonly called alternate-day fasting (ADF). The second form is time-restricted feeding (TRF), eating within specific time frames such as the most prevalent 16: 8 diet, with 16 hours of fasting and 8 hours for eating. It is already known that IF can bring about changes in metabolic parameters related with type 2 diabetes (T2D). Furthermore, IF can be effective in improving health by reducing metabolic disorders and age-related diseases. However, it is not clear yet whether the age at which fasting begins, gender and severity of T2D influence on the effectiveness of the different types of IF in reducing metabolic disorders. In this chapter I will present the risk factors of T2D, the different types of IF interventions and the research-based knowledge regarding the effect of IF on T2D. Furthermore, I will describe several machine learning approaches to provide a recommendation system which reveals a set of rules that can assist selecting a successful IF intervention for a personal case. Finally, I will discuss the question: Can we predict the optimal IF intervention for a

**Keywords:** machine learning, decision tree, type 2 diabetes, insulin resistance,

Obesity is an epidemic in developed countries. The obesity epidemic is increasing its magnitude and its public health impact. In 2017–2018, 67% of the population in Australia were overweight or obese [1]. In the United States, only minority of the individuals have a healthy weight (body mass index (BMI) of

(WHO), nearly 2 billion adults are overweight and more than 600 million patients are obese [3]. Type 2 Diabetes (T2D) is one of the chronical diseases associated with Obesity. T2D is usually characterized by insulin resistance (IR) [4]. Insulin resistance (IR) happens when the body does not fully respond to insulin. IR level can be used as a filtering index for primary T2D prevention.

, [2]. Furthermore, according to the World Health Organization
